Retrofitting Towards Net-Zero Energy Building Under Climate Change: An Approach Integrating Machine Learning and Multi-Objective Optimization

Achieving Net-Zero Energy Building (NZEB) performance through retrofitting requires identifying optimal measures that effectively enhance energy efficiency. Determining these optimal retrofit strategies typically involves running thousands of building energy simulations, which imposes a substantial computational burden. To address this challenge, a novel machine learning-based framework is proposed to optimize retrofit strategies for NZEBs under future climate change scenarios. A Non-Dominated Sorting Genetic Algorithm (NSGA-III) is employed to minimize both annual energy consumption and the Predicted Percentage of Dissatisfied (PPD), while simultaneously ensuring net-zero energy balance, thereby generating a Pareto front of optimal solutions. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is then applied to rank the Pareto-front solutions and identify the most favorable retrofit scenario. The results show that the proposed framework reduces optimization time by at least a factor of two compared with simulation-only optimization. Leveraging these computational savings, the framework evaluates a suite of passive and renewable measures across multiple future timeframes to capture the influence of climate change on retrofit performance. The findings indicate that achieving NZEB under future climate conditions requires higher levels of thermal insulation and greater renewable integration than under present-day conditions. Under the Shared Socioeconomic Pathways (SSP) framework, optimal insulation levels in the fossil fuel-dependent scenario are lower than in the sustainable scenario by up to 18% in C-type (warm temperate), 12% in D-type (snow), and 13% in E-type (polar) climates. The combined retrofit measures can reduce annual energy consumption by up to 80% and lower PPD by as much as 67% compared to the base case.